Database Management Systems
When working with aggregate functions in SQL Server, it’s easy to overlook that certain datatypes have strict limits. If you’re summing values in large tables (or even just summing very large numbers), you might run into an arithmetic overflow error. This happens when the result of an aggregate exceeds the maximum value the datatype can hold.
Understanding how this works and how to prevent errors will help you write reliable queries.
When you’re working with real-world data, duplicates can sometimes sneak in more often than we’d like. Maybe an import process didn’t filter things properly, or users managed to submit the same information twice. Whatever the reason, finding and handling duplicates is a common task.
A neat way to tackle this problem in SQL Server is by using window functions. Specifically, we can use ROW_NUMBER() or RANK() to find duplicate rows in a table. These functions let you assign a sequence number to rows within a group, which makes it easy to spot duplicates.
When dealing with applications that span multiple time zones, you’ll often want to store timestamps in UTC. That’s usually the best practice – it avoids confusion and ensures consistency. But sooner or later you’ll need to show users dates and times in their own local time zones. There are a few ways to handle this in SQL Server.
When working with SQL Server, numeric precision can become a silent troublemaker if you’re not careful. Overflow errors happen when a number exceeds the storage capacity of the column data type. Unlike other errors that are easy to catch in testing, numeric overflow often shows up unexpectedly in production, which can be costly. Understanding how to pick the right precision and scale for your NUMERIC or DECIMAL columns can save you headaches down the road.
Working with dates and times in SQL Server can get tricky, especially once you add time zones into the mix. One common point of confusion is the difference between the server’s time zone and the session’s time zone. If you’re not careful, you can end up with inconsistent data or timestamps that don’t line up with what your users expect.
Let’s look at how SQL Server treats time zones and how you can handle server versus session time zone differences cleanly.
Both CONVERT() and TRY_CONVERT() in SQL Server are used to convert data types, but they behave quite differently when something goes wrong. Understanding that difference can save you a lot of debugging time, especially when dealing with messy or unpredictable data.
Let’s look at when you should use each, and walk through an example that you can run yourself.
When working with dates in SQL Server, you’ll quickly find that there are different ways to calculate the “week number” of a given date. Two of the most relevant systems are the ISO week (ISO 8601 standard) and what we’ll call the US week numbering system. If you’re working in a US environment, the latter is often the default approach (due to your session’s language setting), while ISO weeks are the international standard. These different week numbering systems will often produce different results, which can be confusing if you don’t know why.
Let’s walk through the difference, and then we’ll test it with some simple examples.
When you’re putting together reporting dashboards, raw datetime values like 2025-09-23 13:45:32.000 don’t do much for the average business user. People want to see “Sep 2025” or “Tuesday, September 23, 2025” rather than a timestamp that looks like it came straight from the database.
In many cases, formatting can also be handled in the reporting or application layer, which may be better for things like localization and display preferences. But there are plenty of situations where it makes sense to do this work in SQL Server itself. For example, maybe you need consistency or business-specific date logic. Fortunately, SQL Server gives us several tools for shaping dates into clear, readable labels that work well in dashboards.
Working with JSON data in SQL Server has become quite common as more applications are relying heavily on semi-structured data. When your JSON includes nested objects or arrays, it’s useful to know how to make precise updates without rewriting the entire document. Fortunately, SQL Server makes this relatively simple with the JSON_MODIFY() function. This function lets you update specific values inside JSON text stored in a table, even deep within the structure.
In this article, we’ll walk through an example of how to use SQL Server’s JSON_MODIFY() function to update nested JSON.
String manipulation in SQL Server can sometimes be tricky, especially when you don’t know exactly where the piece of text you need begins or ends. You might have data where the structure isn’t perfectly consistent, and you can’t rely on fixed positions. That’s where dynamic substring extraction comes in handy.
In this article we’ll look at how we can dynamically extract substrings from a string in SQL Server when we don’t know the start or end positions of those substrings, or their lengths.